{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import graphviz\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import tree\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.model_selection import train_test_split, cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-18.08941176, -10.61843137, -16.31843137, -44.97803922,\n",
       "       -17.12509804, -49.71509804, -12.9986    , -88.4514    ,\n",
       "       -55.7914    , -25.0816    ])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston = load_boston()\n",
    "regressor = tree.DecisionTreeRegressor(random_state=0)\n",
    "cross_val_score(regressor, boston.data, boston.target, cv=10, scoring=\"neg_mean_squared_error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 绘制回归图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(1)\n",
    "x = np.sort(5 * rng.rand(80,1), axis=0)\n",
    "y = np.sin(x).ravel()\n",
    "y[::5] += 3 * (0.5 - rng.rand(16))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f7d98aa2668>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(x, y, s=20, edgecolor=\"black\",c=\"darkorange\", label=\"data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=5)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr_1 = tree.DecisionTreeRegressor(max_depth=2)\n",
    "regr_2 = tree.DecisionTreeRegressor(max_depth=5)\n",
    "regr_1.fit(x, y)\n",
    "regr_2.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]\n",
    "y_1 = regr_1.predict(X_test)\n",
    "y_2 = regr_2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(x, y, s=20, edgecolor=\"black\",c=\"darkorange\", label=\"data\")\n",
    "plt.plot(X_test, y_1, color=\"cornflowerblue\",label=\"max_depth=2\", linewidth=2)\n",
    "plt.plot(X_test, y_2, color=\"yellowgreen\", label=\"max_depth=5\", linewidth=2)\n",
    "plt.xlabel(\"data\")\n",
    "plt.ylabel(\"target\")\n",
    "plt.title(\"Decision Tree Regression\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
